Machine learning analysis for predicting performance in female volleyball players in India: Implications for talent identification and player development strategies

Abstract

Talent identification and player development are crucial aspects of sports management, particularly in volleyball, where understanding players' performance predictors is essential. The primary objective is to investigate the relationships between players' demographic and physical attributes and their on-court performance, providing valuable insights for talent identification and player development strategies. The dataset comprises demographic and physical attributes alongside performance metrics of college-level female volleyball players in India. Data were meticulously collected from various institutions participating in volleyball tournaments across India. Three machine learning algorithms—linear regression, random forest regression, and XGBoost regression—were trained using the pre-processed dataset. Standard regression evaluation metrics such as mean squared error (MSE), root mean squared error (RMSE), and R-squared (R2) score were used to assess model performance. Random forest regression emerged as the top-performing ML technique, achieving a prediction accuracy of 94.18%, followed by XGBoost regression with 92.76%. Height, muscle mass, and bone mass exhibited strong positive correlations with performance prediction, emphasizing their significance. This study highlights ML techniques' potential, particularly random forest regression, in improving talent identification and performance prediction in college-level female volleyball players in India.